人工智能和机器学习用于急性胰腺炎的精准医学:叙述性审查
Sandra López Gordo1,2, Elena Ramirez-Maldonado3,4, Maria Teresa Fernandez-Planas5
1General and Digestive Surgery Department, Maresme Health Consortium, 08304 Mataro, Spain.
Medicina (Kaunas, Lithuania)
|April 26, 2025
概括
人工智能和机器学习显著提高急性胰腺炎 (AP) 管理. 与传统的评分系统相比,人工智能模型在预测严重程度,并发症和死亡率方面提供了更高的准确性,改善了患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 急性胰腺炎 (AP) 构成一个重大的临床挑战,严重程度可变,并可能导致危及生命的并发症.
- 传统的评分系统 (如Ranson,BISAP) 提供了基本的风险分层,但往往缺乏早期的准确性.
- 需要先进的工具来提高诊断的准确性,严重程度的预测和AP的管理.
研究的目的:
- 探索人工智能 (AI) 和机器学习 (ML) 在增强急性胰腺炎 (AP) 管理中的作用.
- 评估AI/ML在诊断,严重程度预测,并发症管理和AP治疗优化的应用.
- 在预测AP结果时,将AI/ML模型的性能与传统的评分系统进行比较.
主要方法:
- 对最近的研究进行了全面的分析,重点是AP中的AI和ML应用.
- 包括ML模型,如XGBoost,神经网络和多式联络方法.
- 整合临床,实验室,成像数据和放射学特征,用于模型开发和验证.
主要成果:
- 与传统方法相比,基于人工智能的模型显示出更高的诊断和预后准确性.
- 在早期严重急性胰腺炎 (SAP) 预测方面,XGBoost的AUC为0.93,超过了BISAP (0.74) 和APACHE II (0.81).
- 人工智能模型在预测死亡率 (AUC 0.975) 和检测急性呼吸困扰综合征 (ARDS) (AUC 0.891) 中表现出高准确度.
结论:
- 人工智能和机器学习代表了AP管理的变革性进步,使个性化治疗和早期风险分层成为可能.
- 人工智能有助于优化资源利用,并有可能显著改善AP患者的治疗结果.
- 解决模型通用性和临床采用等挑战对于全球重新定义AP护理标准至关重要.
相关概念视频
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Pancreatitis is the inflammation of the pancreas, which occurs when the immune system becomes active and causes swelling, pain, and disruptions in organ function. Pancreatitis can manifest as either an acute or chronic condition.
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